"""LLM steering prompt mappers for generating evaluation prompts."""

import json
from typing import Any, Protocol

from .....types.tools import ToolUse
from ...core.context import SteeringContext

# Agent SOP format - see https://github.com/strands-agents/agent-sop
_STEERING_PROMPT_TEMPLATE = """# Steering Evaluation

## Overview

You are a STEERING AGENT that evaluates a {action_type} that ANOTHER AGENT is attempting to make.
Your job is to provide contextual guidance to help the other agent navigate workflows effectively.
You act as a safety net that can intervene when patterns in the context data suggest the agent
should try a different approach or get human input.

**YOUR ROLE:**
- Analyze context data for concerning patterns (repeated failures, inappropriate timing, etc.)
- Provide just-in-time guidance when the agent is going down an ineffective path
- Allow normal operations to proceed when context shows no issues

**CRITICAL CONSTRAINTS:**
- Base decisions ONLY on the context data provided below
- Do NOT use external knowledge about domains, URLs, or tool purposes  
- Do NOT make assumptions about what tools "should" or "shouldn't" do
- Focus ONLY on patterns in the context data

## Context

{context_str}

## Event to Evaluate

{event_description}

## Steps

### 1. Analyze the {action_type_title}

Review ONLY the context data above. Look for patterns in the data that indicate:

- Previous failures or successes with this tool
- Frequency of attempts
- Any relevant tracking information

**Constraints:**
- You MUST base analysis ONLY on the provided context data
- You MUST NOT use external knowledge about tool purposes or domains
- You SHOULD identify patterns in the context data
- You MAY reference relevant context data to inform your decision

### 2. Make Steering Decision

**Constraints:**
- You MUST respond with exactly one of: "proceed", "guide", or "interrupt"
- You MUST base the decision ONLY on context data patterns
- Your reason will be shown to the AGENT as guidance

**Decision Options:**
- "proceed" if context data shows no concerning patterns
- "guide" if context data shows patterns requiring intervention
- "interrupt" if context data shows patterns requiring human input
"""


class LLMPromptMapper(Protocol):
    """Protocol for mapping context and events to LLM evaluation prompts."""

    def create_steering_prompt(
        self, steering_context: SteeringContext, tool_use: ToolUse | None = None, **kwargs: Any
    ) -> str:
        """Create steering prompt for LLM evaluation.

        Args:
            steering_context: Steering context with populated data
            tool_use: Tool use object for tool call events (None for other events)
            **kwargs: Additional event data for other steering events

        Returns:
            Formatted prompt string for LLM evaluation
        """
        ...


class DefaultPromptMapper(LLMPromptMapper):
    """Default prompt mapper for steering evaluation."""

    def create_steering_prompt(
        self, steering_context: SteeringContext, tool_use: ToolUse | None = None, **kwargs: Any
    ) -> str:
        """Create default steering prompt using Agent SOP structure.

        Uses Agent SOP format for structured, constraint-based prompts.
        See: https://github.com/strands-agents/agent-sop
        """
        context_str = (
            json.dumps(steering_context.data.get(), indent=2) if steering_context.data.get() else "No context available"
        )

        if tool_use:
            event_description = (
                f"Tool: {tool_use['name']}\nArguments: {json.dumps(tool_use.get('input', {}), indent=2)}"
            )
            action_type = "tool call"
        else:
            event_description = "General evaluation"
            action_type = "action"

        return _STEERING_PROMPT_TEMPLATE.format(
            action_type=action_type,
            action_type_title=action_type.title(),
            context_str=context_str,
            event_description=event_description,
        )
